PromptEmbedder: A Dual-LLM Framework for Efficient, Architecture-Agnostic Text Embedding
By
[Submitted on 27 May 2026]
Plain bagel done well. Pleasantly substantive.
Summary
The article presents PromptEmbedder, a novel dual-LLM framework for efficient and transferable text embedding. It addresses the bottleneck of current methods like LoRA that require costly retraining when new backbone architectures emerge. PromptEmbedder uses a Prompting LLM to generate instruction-aware soft prompts for a frozen Embedding LLM via differentiable generation with continuous relaxation. This decouples embedding knowledge from specific backbone weights, allowing adaptation to new architectures by only retraining a lightweight linear alignment matrix. Evaluated on the MTEB benchmark, PromptEmbedder achieves comparable performance to LoRA finetuning while reducing GPU memory by 40% and accelerating training by 3.7x.
Key quotes
· 4 pulledPromptEmbedder utilizes a Prompting LLM to generate instruction-aware soft prompts for a frozen Embedding LLM via a differentiable generation process with continuous relaxation, ensuring full gradient flow during contrastive training.
By localizing task-specific knowledge within the Prompting LLM, adapting to new architectures requires only retraining a lightweight linear alignment matrix.
Evaluations on the MTEB benchmark show that PromptEmbedder achieves comparable performance with LoRA finetuning while reducing GPU memory by 40% and accelerating training by 3.7x.
Our approach establishes a scalable, architecture-agnostic paradigm for efficient LLM-based representation learning.
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